From c771b3a75a6ebbfbfc398a028a477246b0799cf0 Mon Sep 17 00:00:00 2001
From: Tim Dettmers <tim.dettmers@gmail.com>
Date: Fri, 22 Jul 2022 14:41:05 -0700
Subject: Most tests passing.
---
bitsandbytes/autograd/_functions.py | 307 ++++++++++++++++++++++++++++++++++++
1 file changed, 307 insertions(+)
create mode 100644 bitsandbytes/autograd/_functions.py
(limited to 'bitsandbytes/autograd/_functions.py')
diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py
new file mode 100644
index 0000000..815a4f1
--- /dev/null
+++ b/bitsandbytes/autograd/_functions.py
@@ -0,0 +1,307 @@
+import torch
+import bitsandbytes as bnb
+import bitsandbytes.functional as F
+
+from dataclasses import dataclass
+
+tensor = torch.Tensor
+
+'''
+ This class pools outlier dimensions across layers.
+ This is particularly important for small models where outlier features
+ are less systematic and occur with low frequency.
+'''
+class GlobalOutlierPooler(object):
+ _instance = None
+
+ def __init__(self):
+ raise RuntimeError('Call get_instance() instead')
+
+ def initialize(self):
+ self.outliers = set()
+ self.model_dim = None
+
+ @classmethod
+ def get_instance(cls):
+ if cls._instance is None:
+ cls._instance = cls.__new__(cls)
+ cls._instance.initialize()
+ return cls._instance
+
+ def add_outliers(self, outlier_idx, feature_dim):
+ if self.model_dim is None: self.model_dim = feature_dim
+ if feature_dim != self.model_dim: return # we do not encode outliers for the 2nd FFN layer
+
+ self.outliers.update(outlier_idx.tolist())
+
+ def get_current_outlier_idx(self):
+ return torch.Tensor(list(self.outliers)).to(torch.int64)
+
+class MatMul8bit(torch.autograd.Function):
+
+ @staticmethod
+ def forward(ctx, A, B, out=None, quant_type='vector', precision=[8, 8, 8]):
+
+ if precision[0] != 8:
+ with torch.no_grad():
+ output = torch.matmul(A, B)
+ else:
+ if len(B.shape) == 2: dim = 0
+ else: dim = 1
+ qA, SA = F.vectorwise_quant(A, dim=-1, quant_type=quant_type)
+ qB, SB = F.vectorwise_quant(B, dim=dim, quant_type=quant_type)
+ iout = F.igemm(qA, qB)
+ output = F.vectorwise_mm_dequant(iout, SA, SB, A.dtype, quant_type)
+
+ if A.requires_grad or B.requires_grad:
+ ctx.save_for_backward(A, B)
+
+ ctx.quant_type = quant_type
+ ctx.precision = precision
+
+ return output
+
+ @staticmethod
+ def backward(ctx, grad_output):
+ A, B = ctx.saved_tensors
+ quant_type = ctx.quant_type
+ precision = ctx.precision
+ grad_A = grad_B = None
+
+ if B.requires_grad:
+ if len(A.shape) == 3:
+ dims = [0, 1]
+ # bsi -> ibs
+ permute_dim = [0, 2, 1]
+ else:
+ dims = [0]
+ # bs -> sb
+ permute_dim = [1, 0]
+
+ if precision[1] != 8:
+ with torch.no_grad():
+ grad_B = torch.matmul(A.permute(permute_dim), grad_output)
+ else:
+ if len(B.shape) == 2 and len(A.shape) == 3:
+ grad_output = grad_output.contiguous()
+ if not grad_output.is_contiguous(): grad_output.contiguous()
+ qgrad_output, S1 = F.vectorwise_quant(grad_output.view(-1, grad_output.shape[2]), dim=0, quant_type=quant_type)
+ if not A.is_contiguous(): A = A.contiguous()
+ qA, S2 = F.vectorwise_quant(A.view(-1, A.shape[2]), dim=0, quant_type=quant_type)
+ igrad_B = F.igemm(qA.t(), qgrad_output)
+ grad_B = F.vectorwise_mm_dequant(igrad_B, S2.t(), S1, grad_output.dtype, quant_type)
+ else:
+ qgrad_output, S1 = F.vectorwise_quant(grad_output, dim=dims, quant_type=quant_type)
+ qA, S2 = F.vectorwise_quant(A, dim=dims, quant_type=quant_type)
+ igrad_B = F.igemm(qA.permute(permute_dim), qgrad_output)
+ grad_B = F.vectorwise_mm_dequant(igrad_B, S2.permute(permute_dim), S1, grad_output.dtype, quant_type)
+
+ if A.requires_grad:
+ if len(grad_output.shape) == 3: dims = [2]
+ else: dims = [1]
+
+ if len(B.shape) == 3:
+ # bio -> boi
+ permute_dim = [0, 2, 1]
+ dim_B = dims
+ else:
+ # io -> oi
+ permute_dim = [1, 0]
+ dim_B = [1]
+
+ if precision[2] != 8:
+ with torch.no_grad():
+ grad_A = torch.matmul(grad_output, B.permute(permute_dim))
+ else:
+ qgrad_output, S1 = F.vectorwise_quant(grad_output, dim=dims, quant_type=quant_type)
+ qB, S3 = F.vectorwise_quant(B, dim=dim_B, quant_type=quant_type)
+ igrad_A = F.igemm(qgrad_output, qB.permute(permute_dim))
+ grad_A = F.vectorwise_mm_dequant(igrad_A, S1, S3.permute(permute_dim), grad_output.dtype, quant_type)
+
+ return grad_A, grad_B, None, None, None
+
+
+mm_cublas = MatMul8bit.apply
+bmm_cublas = MatMul8bit.apply
+matmul_cublas = MatMul8bit.apply
+
+@dataclass
+class MatmulLtState:
+ CB = None
+ CxB = None
+ SB = None
+ SCB = None
+
+ CxBt = None
+ SBt = None
+ CBt = None
+
+ subB = None
+
+ outlier_pool = None
+ has_accumulated_gradients = False
+ threshold = 0.0
+ idx = None
+ is_training = True
+ has_fp16_weights = True
+ use_pool = False
+ formatB = F.get_special_format_str()
+
+ def reset_grads(self):
+ self.CB = None
+ self.CxB = None
+ self.SB = None
+ self.SCB = None
+
+ self.CxBt = None
+ self.SBt = None
+ self.CBt = None
+
+
+class MatMul8bitLt(torch.autograd.Function):
+
+ @staticmethod
+ def forward(ctx, A, B, out=None, state=MatmulLtState()):
+ # 1. Quantize A
+ # 2. Quantize B
+ # 3. Matmul
+ # 4. Mixed-precision decomposition matmul
+ # 5. Save state
+ requires_gradA = A.requires_grad
+ requires_gradB = B.requires_grad
+ formatB = state.formatB
+ input_shape = A.shape
+ if state.outlier_pool is None: state.outlier_pool = GlobalOutlierPooler.get_instance()
+ assert A.dtype == torch.float16, f'The input data type needs to be fp16 but {A.dtype} was found!'
+
+ # 1. Quantize A
+ if len(A.shape) == 3: A = A.view(-1, A.shape[-1]).contiguous()
+ CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(A, threshold=state.threshold)
+
+ if state.threshold > 0.0 and coo_tensorA is not None:
+ if state.has_fp16_weights:
+ idx = torch.unique(coo_tensorA.colidx).long()
+ CA[:, idx] = 0
+ CAt[:, idx] = 0
+ subA = A[:, idx]
+ state.subB = B[:, idx].t().contiguous()
+ state.idx = idx
+ else:
+ if state.CxB is None:
+ # B in in 8-bit row-major, we can transform it back to 16-bit to extract outlier dimensions
+ # we also need to convert it to the turing/ampere format
+ state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
+ if state.threshold > 0.0 and coo_tensorA is not None and state.idx is None and state.CB is not None:
+ # generate outlier index and subB
+ outlier_idx = torch.unique(coo_tensorA.colidx).long()
+ state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
+ if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
+ # do not use pool for 2nd FFN layer
+ state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
+ else:
+ state.idx = outlier_idx
+ state.subB = (state.CB[:, state.idx].float().t().contiguous()*(state.SCB/127)).half()
+
+ if state.idx is not None:
+ # extract outliers
+ CA[:, state.idx] = 0
+ CAt[:, state.idx] = 0
+ subA = A[:, state.idx]
+ else:
+ subA = None
+ else:
+ if not state.has_fp16_weights and state.CxB is None:
+ state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
+ subA = None
+
+ C32A, SA = F.transform(CA, 'col32')
+
+ # 2. Quantize B
+ if state.has_fp16_weights:
+ has_grad = (True if (getattr(B, 'grad', None) is not None) else False)
+ is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1)
+ if is_transposed: B = B.contiguous()
+
+ if (state.is_training and not has_grad) or state.CxB is None:
+ state.reset_grads()
+ CB, state.CBt, state.SCB, state.SCBt, coo_tensorB = F.double_quant(B)
+ state.CxB, state.SB = F.transform(CB, to_order=formatB)
+ else:
+ has_grad = False
+
+ shapeB = state.SB[0]
+
+ if len(input_shape) == 3:
+ output_shape = (input_shape[0], input_shape[1], shapeB[0])
+ else:
+ output_shape = (input_shape[0], shapeB[0])
+
+ # 3. Matmul
+ out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB)
+ output = F.mm_dequant(out32, Sout32, SCA, state.SCB)
+
+ # 4. Mixed-precision decomposition matmul
+ if state.threshold > 0.0 and coo_tensorA is not None and subA is not None:
+ output += torch.matmul(subA, state.subB)
+
+ # 5. Save state
+ ctx.state = state
+
+ ctx.formatB = formatB
+ ctx.grad_shape = input_shape
+ ctx.req_grads = [requires_gradA, requires_gradB]
+
+ if requires_gradA or requires_gradB:
+ ctx.tensors = (CAt, subA)
+ ctx.tensor_states = (SCAt, state.idx)
+ else:
+ ctx.tensors = [None, None]
+ ctx.tensor_states = (None, None)
+ ctx.save_for_backward(None, None)
+
+ #clone_func = torch.clone if len(output_shape) == 3 else lambda x : x
+ clone_func = torch.clone
+ return clone_func(output.view(output_shape))
+
+ @staticmethod
+ def backward(ctx, grad_output):
+ req_gradA, req_gradB = ctx.req_grads
+ CAt, subA = ctx.tensors
+ SCAt, idx = ctx.tensor_states
+ formatB = ctx.formatB
+ state = ctx.state
+ assert state.has_fp16_weights, 'Backprop only supported for fp16 weights.'
+
+ if len(grad_output.shape) == 3:
+ grad_output = grad_output.view(-1, grad_output.shape[-1]).contiguous()
+
+ grad_A = grad_B = None
+
+ Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output)
+ if req_gradB:
+ CxAt, SAt = F.transform(CAt, formatB, transpose=True)
+ C32grad, Sgrad = F.transform(Cgradt, 'col32', transpose=True)
+ gradB32, SgradB32 = F.igemmlt(C32grad, CxAt, Sgrad, SAt)
+ grad_B = F.mm_dequant(gradB32, SgradB32, SCgradt, SCAt)
+ if state.threshold > 0.0 and subA is not None:
+ grad_B[:, idx] += torch.matmul(grad_output.t(), subA)
+
+ if req_gradA:
+ C32grad, Sgrad = F.transform(Cgrad, 'col32')
+ if state.CxBt is None:
+ state.CxBt, state.SBt = F.transform(state.CBt, to_order=formatB, transpose=True)
+ gradA32, SgradA32 = F.igemmlt(C32grad, state.CxBt, Sgrad, state.SBt)
+ grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape)
+
+ return grad_A, grad_B, None, None, None, None, None
+
+
+matmul = MatMul8bitLt.apply
+
+
+def matmul(A : tensor, B : tensor, out : tensor=None, state : MatmulLtState = None, threshold=0.0):
+ state = state or MatmulLtState()
+ if threshold > 0.0:
+ state.threshold = threshold
+ return MatMul8bitLt.apply(A, B, out, state)
+
--
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